Constant false alarm rate (CFAR) detection refers to a conventional form of an adaptive algorithm used in sensor systems to detect signals reflected from a target against a background of noise, clutter and interference. A sensor system can use electromagnetic signals, sonar signals, acoustic signals or signals at any other frequency. A false alarm is an erroneous detection. That is a positive determination or decision about the presence of a target based on an interpretation of information in the detected signal when a valid target is not present. A false alarm is often due to background noise or interfering signals, which cause the detection signal to exceed a decision threshold. If the detection decision threshold is set too high, there are very few false alarms, but the reflected signal power required to exceed the decision threshold inhibits detection of valid targets. If the detection decision threshold is set too low, the large number of false alarms that result masks the presence of valid targets.
The false-alarm rate depends on the level of all interference, such as noise, clutter or artificial jammers. Any non-signal related voltage or current in a system is a source of noise. Clutter results from transmitted signals that are reflected from environmental features other than a target of interest (e.g., water, land, structures, etc.). Jammers or jamming signals are non-desired signals generated by a source other than the sensor or sensor system. Detection of sensor targets in shorter distances is usually inhibited by the clutter, while targets at longer distances are affected mostly by the background noise. Thus, the false alarm rate is range dependent. To achieve a higher probability of target detection, the decision threshold should be adapted to the environment. Conventional CFAR detectors employ a “background averaging” technique to dynamically adapt the decision threshold. Specifically for range-Doppler based signal systems, when noise is present in the radar signal, the maximum reflected energy in a cell-under-test is compared to an estimate of the interference (noise, clutter and any jammers) in the cell-under-test. These conventional systems determine an average level of interference from cells adjacent to the cell-under-test. This approach assumes that the clutter and interference is spatially and temporally homogeneous over the cells being used in defining for CFAR implementation. However, this is not the case for many environments.
The value of the adaptive threshold level is a function of the amplitudes in the range-Doppler cells surrounding the specific range-Doppler cell for which the process must derive the adaptive threshold. Furthermore, the number of surrounding range-Doppler cells (data points) needed to effectively compute an adaptive threshold varies with range-to-target, signal emitter-to-target attitude, noise, clutter, and intentional interference (e.g., a jamming signal or jammer) when present. If the environment of the surveillance area is dynamic, the signal processor must continue to vary, or adapt, the number of data points for each unique environmental region in the range-Doppler matrix, thus the term “adaptive threshold.”
As stated above, the range-Doppler matrix typically reflects signal returns over a large surveillance area containing many environmental variations. In order to optimize target detection performance, the signal system's signal processor must be able to apply as many unique parameter sets as necessary to derive adaptive thresholds which accurately reflect each unique environmental region in the range-Doppler matrix. The conventional single instruction multiple data (SIMD) processor must process each unique parameter set in sequence. Since each sequential operation increases the overall time required to process the data stored in the range-Doppler matrix, the signal processor may not have enough time to derive an adaptive threshold for each unique environmental region. As a result, conventional signal processing systems to date have used various techniques to minimize the number of parameter sets used in order to save processing time. The “trade-off” is that the system may be forced to apply less than optimal parameter sets; therefore, less than optimal adaptive thresholds. This ultimately degrades target detection performance.
The concept of adaptive target detection thresholds is not unique. For example, U.S. Pat. No. 4,845,500 to Cornett et al discloses a radar video detector and target tracker in which an adaptive target detection threshold value is calculated for each target on every scan. The threshold values are computed by taking the radar video signals from a target or clutter and averaging the signals over small areas (cells) which are stored in memory for processing. These cells are elements in a matrix ‘n’ azimuth sectors and ‘m’ range bins in dimension. Stored values in the first and last row of cells are processed to establish the mean value and mean deviation value for each row in the window. The smallest values are subtracted from the averaged signals to establish revised amplitudes for each cell with reduced background noise. Each element is compared with its neighboring elements and target detection is indicated in a cell when at least one element of the two adjacent elements has a positive amplitude.
U.S. Pat. No. 4,713,664 to Taylor, Jr., discloses an adaptive threshold system which is used to set the alarm threshold level for Doppler filters. The system uses data corresponding to at least three antenna azimuth positions. The data is derived from adjacent coherent processing intervals in moving target detector (MTD) radar systems. The adaptive threshold level is governed by combinations of three or more azimuth data values in order to make the threshold level more closely match the residue curve rather than the input clutter from a point clutter source. Compensation of the threshold level determined from the three azimuth data values is provided by signals from the zero Doppler filter output. Additional compensation is provided for other system variables, such as changes in the scan rate, radar instability, and conventional constant false alarm rate processing. The threshold system combines the largest of the clutter input values with the compensating signals by use of a log power combiner to provide the combined and compensated threshold level.
U.S. Pat. No. 4,486,756 to Peregrim et al. discloses a method of reducing angle noise in a radar system. Energy is transmitted in an arbitrarily chosen frequency pair symmetrically disposed about the tuning frequency of the radome of the radar, and the complex monopulse ratios of the return signals are formed. The sum magnitude and the magnitude of the imaginary part of the complex monopulse ratio, determined for each frequency pair, are subjected to selected thresholds in order to reject erroneous data points. A sum channel threshold and a threshold on the imaginary part of the complex monopulse ratios are utilized. Both of these thresholds vary as a function of the missile-to-target range. In addition, a glint threshold is also utilized. The glint threshold is an adaptive threshold predicated on a desired probability of false alarm.
U.S. Pat. No. 3,720,942 to Wilmot et al. discloses a system for automatically processing quantized normal and moving target indicator (MTI) radar video to provide improved clutter rejection and improved detection of moving targets in clutter. The quantized video is applied to a mean level detector. The sensitivity of the mean level detector is controlled as a function of the number of detected target reports being stored in an output buffer unit in order to provide the proper threshold. The output of the mean level detector and the quantized normal video are applied to a video selector circuit for automatic selection of subsequent detection and processing.
U.S. Pat. No. 5,465,095 to Bryant discloses a system that subdivides the range-Doppler matrix into several equally-sized elements. The radar system performs a process on the equally-sized elements in parallel. The process involves an integration process implemented over each cell in an element. This yields a secondary data array of equal dimension to the original element. Target detection thresholds for each cell are determined from the information in the secondary data arrays.
Although these patents relate to various methods for processing radar signals and enhancing target detection, they do not describe an efficient process for computing a generalized adaptive target detection threshold.